Operator-ready prompt for reuse, tuning, and workspace runs.
This item is set up for developers who want to inspect the original language, fork it into Workspace, and adapt the evidence model without losing the source prompt structure.
Implementation handoffs, eval setup, and prompt tuning where you need the original structure intact.
Inspect first, copy once, then fork into Workspace when you want variants, notes, and model settings attached to the same run.
Swap domain facts, examples, and any hard-coded entities for your own context.
Tighten the evidence or verification requirement if this is headed toward production.
Decide which failure mode you want to evaluate first before you branch the prompt.
This prompt already carries implementation detail, tool context, and a final-output instruction. Keep that structure intact when you tune it, or your comparison runs get noisy fast.
Open this prompt inside Workspace when you want a live iteration loop.
Copy for quick reuse, or run it in Workspace to keep prompt variants, model settings, and prompt-history changes in one place.
Structured source with 1 active lines to adapt.
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Prompt content
Original prompt text with formatting preserved for inspection and clean copy.
Select the top 3-5 candidates based on predicted properties. Using Semantic Kernel, implement an iterative refinement loop with Llama 3.1. Guide the model to modify structures to improve multi-target affinity, reduce ADMET risks, and maintain drug-likeness. Integrate Tavily API to perform targeted searches for relevant information (e.g., known pharmacophores, protein-ligand interactions) during this refinement process. Ensure all iterations and changes are logged in Weights & Biases.
Adaptation plan
Keep the source stable, then branch your edits in a predictable order so the next prompt run is easier to evaluate.
Hold the task contract and output shape stable so generated implementations remain comparable.
Update libraries, interfaces, and environment assumptions to match the stack you actually run.
Test failure handling, edge cases, and any code paths that depend on hidden context or secrets.
Copy once for a pristine source snapshot, then move the prompt into Workspace when you want variants, run history, and side-by-side tuning without losing the original.
Prompt diagnostics
Quick signals for how structured this prompt already is and where adaptation work is likely to happen first.
This prompt is mostly narrative and instruction-driven, so you can adapt examples and output constraints first without disturbing the structure.
Generative AI-Driven Optimization for Novel Multi-Target Drug Candidates
Develop an intelligent drug discovery platform capable of generating and optimizing novel small-molecule drug candidates for multi-target activity. This challenge involves leveraging advanced Generative AI and cheminformatics tools to predict binding affinities, assess drug-likeness, and evaluate ADMET profiles. Participants will build a sophisticated agent using Llama 3.1 405B via Groq and Semantic Kernel to orchestrate the design process. The platform will iteratively propose molecular modifications, predict their properties, and refine candidates based on specific therapeutic targets (e.g., GLP-1R, GIPR, GCGR). Experiment tracking will be managed with Weights & Biases, and external knowledge will be integrated using Tavily.
Use the challenge page to recover the original task boundaries before you tune the prompt. That keeps your variants grounded in the same evaluation target instead of drifting into a different problem.